Neural networks : the official journal of the International Neural Network Society
-
Imbalance problem occurs when the majority class instances outnumber the minority class instances. Conventional extreme learning machine (ELM) treats all instances with same importance leading to the prediction accuracy biased towards the majority class. To overcome this inherent drawback, many variants of ELM have been proposed like Weighted ELM, class-specific cost regulation ELM (CCR-ELM) etc. to handle the class imbalance problem effectively. ⋯ The proposed work has lower computational overhead compared to CCR-ELM. The proposed work is evaluated using benchmark real world imbalanced datasets downloaded from the KEEL dataset repository. The results show that the proposed work has better performance than weighted ELM, CCR-ELM , EFSVM, FSVM, SVM for class imbalance learning.